Using Embodied Data for Localization and Mapping

Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping SLAM community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.

[1]  Sebastian Thrun,et al.  Integrating Grid-Based and Topological Maps for Mobile Robot Navigation , 1996, AAAI/IAAI, Vol. 2.

[2]  Mathew H. Evans,et al.  Tactile SLAM with a biomimetic whiskered robot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  Roland Siegwart,et al.  Haptic terrain classification for legged robots , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Joachim Hertzberg,et al.  Landmark-based autonomous navigation in sewerage pipes , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

[5]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[6]  R. Farrenkopf.  Analytic Steady-State Accuracy Solutions for Two Common Spacecraft Attitude Estimators , 1978 .

[7]  Frank Kirchner,et al.  Robot design for space missions using evolutionary computation , 2009, 2009 IEEE Congress on Evolutionary Computation.

[8]  Frank Kirchner,et al.  SpaceClimber: Development of a Six-Legged Climbing Robot for Space Exploration , 2010, ISR/ROBOTIK.

[9]  Udo Frese,et al.  Interview: Is SLAM Solved? , 2010, KI - Künstliche Intelligenz.

[10]  Javier González,et al.  Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM , 2008, IEEE Transactions on Robotics.

[11]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[12]  Karl Iagnemma,et al.  Terrain Classification and Classifier Fusion for Planetary Exploration Rovers , 2008, 2007 IEEE Aerospace Conference.

[13]  Stergios I. Roumeliotis,et al.  Vision‐aided inertial navigation for pin‐point landing using observations of mapped landmarks , 2007, J. Field Robotics.

[14]  Achint Aggarwal,et al.  Tactile Sensors Based Object Recognition and 6D Pose Estimation , 2012, ICIRA.

[15]  Fredrik Gustafsson,et al.  Terrain navigation using Bayesian statistics , 1999 .

[16]  Andreas Zell,et al.  Comparison of Different Approaches to Vibration-based Terrain Classification , 2007, EMCR.

[17]  Regis Hoffman,et al.  Terrain mapping for outdoor robots: robust perception for walking in the grass , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[18]  Andreas Zell,et al.  High resolution visual terrain classification for outdoor robots , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[19]  Edwin Olson,et al.  Robust and efficient robotic mapping , 2008 .

[20]  N. D. Tillett,et al.  Ground based sensing systems for autonomous agricultural vehicles , 2000 .

[21]  Tucker R. Balch,et al.  AuRA: principles and practice in review , 1997, J. Exp. Theor. Artif. Intell..

[22]  Frank Kirchner,et al.  Intelligent Mobility , 2011, KI - Künstliche Intelligenz.

[23]  Joachim Hertzberg,et al.  Evolving interface design for robot search tasks: Research Articles , 2007 .

[24]  Frank Kirchner,et al.  eSLAM—Self Localisation and Mapping Using Embodied Data , 2010, KI - Künstliche Intelligenz.

[25]  Andreas Zell,et al.  SVMs for Vibration-Based Terrain Classification , 2007, AMS.

[26]  Rüdiger Dillmann,et al.  Localization of Walking Robots , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[27]  Daniel D. Lee,et al.  Proprioceptive localilzatilon for a quadrupedal robot on known terrain , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[28]  Wolfram Burgard,et al.  Exploration with active loop-closing for FastSLAM , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[29]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Michael Bosse,et al.  Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework , 2004, Int. J. Robotics Res..

[31]  Cyrill Stachniss,et al.  Hierarchical optimization on manifolds for online 2D and 3D mapping , 2010, 2010 IEEE International Conference on Robotics and Automation.

[32]  Michael J. Swain,et al.  Indexing via color histograms , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[33]  David Wettergreen,et al.  Segmented SLAM in three-dimensional environments , 2010 .

[34]  K. Lingemann,et al.  A Heuristic Loop Closing Technique for Large-Scale 6D SLAM , 2011 .

[35]  Joe P. Golden,et al.  Terrain Contour Matching (TERCOM): A Cruise Missile Guidance Aid , 1980, Optics & Photonics.

[36]  Juan D. Tardós,et al.  Hierarchical SLAM: real-time accurate mapping of large environments , 2005, IEEE Transactions on Robotics.

[37]  Daniel M. Helmick,et al.  Terrain Adaptive Navigation for planetary rovers , 2009 .

[38]  Wolfram Burgard,et al.  Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[39]  M. Broxton,et al.  Ames Stereo Pipeline, NASA's Open Source Automated Stereogrammetry Software , 2010 .

[40]  Pietro Perona,et al.  Fast Terrain Classification Using Variable-Length Representation for Autonomous Navigation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Karl Iagnemma,et al.  Self‐supervised terrain classification for planetary surface exploration rovers , 2012, J. Field Robotics.

[42]  Joachim Hertzberg,et al.  6D SLAM—3D mapping outdoor environments , 2007, J. Field Robotics.

[43]  Pere Ridao,et al.  A survey on Terrain Based Navigation for AUVs , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[44]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[45]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[46]  F. Kirchner,et al.  A Versatile Stair-Climbing Robot for Search and Rescue Applications , 2008, 2008 IEEE International Workshop on Safety, Security and Rescue Robotics.

[47]  Nobuyuki Kita,et al.  3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[48]  Wolfram Burgard,et al.  Improving robot navigation in structured outdoor environments by identifying vegetation from laser data , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[49]  L. B. Hostetler,et al.  Nonlinear Kalman filtering techniques for terrain-aided navigation , 1983 .

[50]  Siddhartha S. Srinivasa,et al.  Proprioceptive Localization for Mobile Manipulators , 2010 .

[51]  Jakob Schwendner,et al.  Self localisation using embodied data for a hybrid leg-wheel robot , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[52]  T. T. Baber,et al.  Modeling General Hysteresis Behavior and Random Vibration Application , 1986 .

[53]  Steven Dubowsky,et al.  Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers , 2004, IEEE Transactions on Robotics.

[54]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[55]  Stergios I. Roumeliotis,et al.  Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks: Research Articles , 2007 .

[56]  Frank Kirchner,et al.  Development of the six‐legged walking and climbing robot SpaceClimber , 2012, J. Field Robotics.

[57]  Ken Perlin,et al.  Improving noise , 2002, SIGGRAPH.

[58]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[59]  Hugh Durrant-Whyte,et al.  Localization of Autonomous Guided Vehicles , 1996 .